Conventionally, fish finders used for ships, which determines a sea bottom sediment type (rocks, stones, sand, etc.) and displays the sediment type has been known (see JP2008-275351A). The bottom sediment determination is performed by analyzing a sea bottom echo of the transmission pulse of an ultrasonic wave. For example, in a location where the sea bottom is hard like rocks or stones with a rolling surface, a time width of the sea bottom echo is longer and, on the other hand, in a location where the sea bottom is soft like sand or mud with a relatively flat surface, the time width of the sea bottom echo is shorter. On a display screen, a similarity with each bottom sediment type and the most similar bottom sediment type among the bottom sediment types are typically displayed. Such a device disclosed in JP2008-275351A uses a neural network to calculate the similarities of the bottom sediment types; thereby more exact bottom sediment determination is possible.
Since echoes differ for every ocean space even when the bottom sediment types are same, it is desired that the neural network learns from the determination result to modify itself every time each ocean space is examined. The neural network updates (learns) connection weights (weighting coefficients) according to correctness of an output value to improve an accuracy rate of the output value.
However, in the bottom sediment determination, it is difficult to know whether the determination result is correct. For this reason, the device disclosed in JP2008-275351A cannot make the connection weights in the neural network into ideal values and, thus, it is difficult to further improve the accuracy rate of the bottom sediment determination.